As in the existing studies [1]}, [2]}, [3]}, [4]}, [5]}, LOCA aggregates the final predictions from the multiple local models by aggregating their weights, where the weight is proportional to the strength of membership of the target user in the local model. Unlike traditional models, however, LOCA uses a weight set \(\mathcal {W}\)  that is different from \(\mathcal {T}\)  used for training. A tighter (smaller) threshold \(h_W\)  in than \(h_T\)  in Eq. (REF ) ensures that the inference relies strictly on similar sets of users.
